This application addresses the broad Challenge Area: 15 Translational Science and the specific Challenge area Topic: 15-RR-101* Applied Translational Technology Development Memory declines, especially in recall, are hallmarks of healthy aging and conversion to cognitive impairment. Our goal is to use highly sensitive mathematical modeling techniques to improve the ability of clinical recall tests to predict future cognitive impairment and to diagnose current impairment. Our research will focus on one of the most widely used clinical tests of such declines, the Rey Auditory Verbal Learning Test (RAVLT). Our specific aims are to apply mathematical models to RAVLT data in order to: (a) substantially improve the ability of the RAVLT and similar clinical recall tests to predict future impairment and to diagnose current impairment;(b) separate different clinically important components of memory from one another in accordance with current theories of the memory processes that underlie performance on the RAVLT and similar tests;(c) identify the components of memory that differentiate cognitive changes that are associated with normal aging from changes that are associated with conversion to impairment;and (d) provide separate scores for different memory components of RAVLT data, which can be used to better predict behavioral and biological markers of future impairment and to identify current impairment. The research will consist of 2 phases, spanning 2 years. Both phases will rely on mathematical modeling tools and software that we have already developed. Our preliminary studies have shown that RAVLT-type tests are inherently noisy measures of impairment because 3 different memory processes are responsible for performance, but only 1 of them (gist-based reconstruction) is responsible for conversion to impairment. Therefore, in both phases of research, we will investigate how predictive and diagnostic power are improved when our modeling tools are used to remove this noise. Noise will be removed by computing separate scores for the reconstruction component of performance and for the other 2 components (direct access of verbatim traces and meta-cognitive confidence). During Phase I, this question will be investigated using a very large sample of subjects who participated in the Aging, Demographics, and Memory Study (ADAMS) portion of NIA's Healthy Retirement Study. The first phase will establish whether noise-free scores greatly improve our ability to separate groups of subjects that differ on biological markers of impairment (e.g., the ApoE genotype), behavioral markers of impairment (e.g., neuropsychological tests), and clinical diagnoses of impairment. During Phase II, this question will be investigated in a longitudinal study of 200 adults (aged 70 and above) who will be administered a neuropsychological test battery, and who will also be administered 3 versions of the RAVLT, spaced at 6-month intervals. The second phase will establish whether noise-free scores greatly improve our ability to differentiate individual people who differ in biological markers of impairment, behavioral markers of impairment, and clinical diagnoses of impairment. PUBLIC HEALTH RELEVANCE: This research will apply state-of-the-art mathematical models to clinical tests of memory to dramatically improve such tests'ability to predict future cognitive impairment in older adults and to diagnose current impairment. Findings will be used to develop low-burden tools that remove the noise for such tests and provide scores for the component memory process that are associated with conversion to impairment.